Using ODA in the Evaluation of Randomized Controlled Trials: Application to Survival Outcomes

Ariel Linden & Paul R. Yarnold

Linden Consulting Group, LLC & Optimal Data Analysis, LLC

In a recent series of papers, ODA has been applied to observational data to draw causal inferences about treatment effects. Presently, ODA is applied to survival outcomes from a randomized controlled trial, with a reanalysis of a study by Linden and Butterworth [2014] that investigated (as a secondary outcome) the effect of a comprehensive hospital-based intervention in reducing mortality at 90 days for chronically ill patients. In the original analysis, differences in mortality rates between treatment and control groups were estimated using logistic regression and calculated as both risk differences and risk ratios, and a treatment effect was found in the subgroup of patients with chronic obstructive pulmonary disease (COPD), but not in the other subgroup of patients with congestive heart failure (CHF). In the present study, we reanalyze these results using both Cox regression, and weighted ODA, wherein the weight of every subject is their follow-up time (i.e., number of days of follow-up).

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Using ODA in the Evaluation of Randomized Controlled Trials

Ariel Linden & Paul R. Yarnold

Linden Consulting Group, LLC & Optimal Data Analysis, LLC

In a recent series of papers, ODA has been applied to observational data to draw causal inferences about treatment effects. In this article ODA is applied to data from a randomized controlled trial, with a reanalysis of a study by Linden and Butterworth [2014] that investigated the effect of a comprehensive hospital-based intervention in reducing readmissions for chronically ill patients. In the original analysis, negative binomial regression was used to evaluate readmission rates and emergency department visit rates at 30 and 90 days, and no treatment effects were found. However, ODA is a superior analytic approach because of its insensitivity to skewed data, model-free permutation tests to derive P values, identification of the threshold value which best discriminates intervention and control groups, use of a chance- and complexity-corrected indexes of classification accuracy, and cross-validation to assess generalizability of the findings.

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Obtaining LOO p in Analysis Involving Three or More Class Categories

Paul R. Yarnold

Optimal Data Analysis, LLC

For class variables with two categories, ODA and MegaODA software employ Fisher’s one-tailed exact test to assess p associated with LOO classification performance. For class variables having three or more categories, LOO p is not provided. This article discusses how to use ODA and MegaODA software to obtain LOO p in this situation.

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ANOVA with Three Between-Groups Factors vs. Novometric Analysis

Paul R. Yarnold

Optimal Data Analysis, LLC

ANOVA with three between-groups factors is used to compare an ordered attribute (score) between four independent categories of class variable “A”, three independent categories of class variable “B”, and two independent categories of class variable “C”. The novometric multiple regression analogue is demonstrated.

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The Australian Gun Buyback Program and Rate of Suicide by Firearm

Ariel Linden & Paul R. Yarnold

Linden Consulting Group, LLC & Optimal Data Analysis, LLC

In 1997, Australia implemented a gun buyback program that reduced the stock of firearms by around one-fifth, and nearly halved the number of gun-owning households. Leigh and Neill evaluated if the reduction in firearms availability affected homicide and suicide rates, and reported that the buyback led to a drop in the firearm suicide rates of almost 80%, with no significant effect on non-firearm death rates. In this paper we re-evaluate the suicide rate data to assess whether any directionally-correct structural breaks in the time series could be identified prior to the buy-back program. Such a change in the time series prior to the intervention may confuse causal interpretation of the actual intervention.

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